The pervasiveness of cell phones has made them an ideal platform for providing many services centred on improving local living conditions. For example SMSs have been used to inform women about prenatal care some countries. Similarly, have been offered HIV/AIDS and TB education, as well as awareness programs for men and women in South Africa via cell phones. This initiative resulted in an increase of 350% in the volume of calls to their hotline. While some of these programs offer gender-neutral solutions, it is clear that many applications would be implemented most effectively with prior knowledge of the gender of the person at the receiving end of the service.
Therefore, gender characterization and automatic gender identification raises as two of the most critical needs for improving cell phone-based services.
Gender characterization has been investigated by the human-computer interaction (HCI) and psychological communities. For instance, female cell phone users in the UK were found to be more comfortable than males making or receiving personal calls in different social contexts (Turner, M.; Love, S.; and Howell, M. 2008. Understanding emotions experienced when using mobile phone in public: “The social usability of mobile (cellular) phones. Telematics and Informatics 25(3):201-215). Similarly, research has demonstrated that males in West Africa tend to use cell phones for job-related tasks as opposed to females who tend to use them for personal calls (Huyer, et al. 2006. Women in the information society. In From the Digital Divide to Digital Opportunities).
The topic of gender identification (or gender classification) has been extensively studied by the computer vision and speech processing communities. Gender recognition methods that use images and voice to identify the gender of a person or face recognition technologies have already been developed.
From an algorithmic point of view, computer vision algorithms use high resolution images to figure out the gender of a person based on its facial features. These algorithms use as input databases of images labelled as male or female to build models that identify facial features that are unique to males or females. The models are then used as a black-box system to identify the gender of a person based on its facial picture. The algorithms used typically are general models like decision trees, neural networks or support vector machines (SVMs).
Similarly, speech recognition systems use the voice of a person to identify its gender. These systems typically use as input a database of pre-recorded conversations labelled as male or female. These pre-recorded conversations are then used as a training set to build a model that identifies voice features that can be uniquely associated to either male or female voices. The model is then used as part of a black-box system that receives as input a voice and outputs a gender label associated to that voice.
Hence, it is both through a better understanding of gender-related differences in the use of technology (gender characterization) and the correct identification of the gender of specific cell phone users (gender identification) that cell phone-based services can be improved. However, these techniques cannot be applied to cell phone networks mostly due to privacy concerns.
Other studies of mobile phone usage have revealed clear gender imbalances, as in Uganda (Diga, K. 2008. Technology spending patterns and poverty level change among households in Uganda. In Workshop on the Role of Mobile Technologies in Fostering Social Development). In particular, Diga has shown that there exists an unequal partner control and usage of the cell phone, specially inclined towards male ownership. Comparable results have been also obtained by Huyer, whose analysis examined the use of cell phones and internet in West Africa. These authors also found that men tend to use cell phones for professional or work-related tasks, while females favour social and personal calls. A recent study in India, Mozambique and Tanzania concluded that males use cell phones with a higher frequency than females, probably because of social norms and financial considerations (Souter et al. 2005. The economic impact of telecommunications on rural livelihoods and poverty reduction. In Commonwealth Telecommunications Organization for UK Department for International Development). In addition, the authors observed that men appear to regard cell phones more highly than women, particularly for business activities. Intriguingly, other studies have shown that the gender gap in cell phone usage is narrowing, with men and women reporting nearly identical calling behaviours. In a gender-based study of cell phone usage in Pakistan, India, Sri Lanka, Philippines and Thailand, Zainudeen showed that for all countries, except for Pakistan, women have similar call frequencies, call destinations and call durations as men (Zainudeen et al. 2008. Who's got the phone? the gendered use of telephones at the BOP. In Annual meeting of the International Communication Association).
Although these studies offer important insights that can be helpful towards gender characterization, such results are typically based on questionnaires applied to a limited amount of individuals. Taken together, previous research works highlight the existence of gender-based differences as well as similarities in calling behaviours. Nevertheless, such studies typically come from the field of psychology based on results that are usually derived from a limited number of personal interviews and/or questionnaires). However, these approaches for gender identification algorithms require access to the content of private conversations or private images, which in the context of cell phone networks is not feasible due to privacy concerns. In particular, speech recognition algorithms require access to the content of private conversations which would violate individual privacy rights unless the user specifically agrees to collaborate. Similarly, the use of pictures or images also suffers from being a very intrusive technique and only feasible if the cell phones are equipped with cameras. So a non-intrusive identification of the gender of a cell phone subscriber is needed.